A knowledge-based tuning method for injection molding machines

Complexity of manufacturing processes has hindered methodical specification of machine setpoints for improving productivity. Traditionally in injectio n molding, the machine set-points are assigned by trial and error, based on heuristic knowledge of an experienced operator, or according to an empiric al model between the inputs and part quality attributes, which is obtained from statistical design of experiments (DOE). In this paper, a Knowledge-Ba sed Tuning (KBT) Method is presented which takes advantage of the a priori knowledge of the process, in the form of a qualitative model, to reduce the demand for experimentation. The KBT Method provides an estimate of the pro cess feasible region (process window) as the basis of finding the suitable setpoints, and updates its knowledge-base using the data that become availa ble during timing. As such, the KBT Method has several advantages over conv entional timing methods: (1) the qualitative model provides a generic form of representation for linear and nonlinear processes alike, therefore, ther e is no need for selecting the form of the empirical model through trial an d error, (2) the use of a priori knowledge eliminates the need for initial trials to construct an empirical model, so an initial feasible region can b e identified us the basis of search for the suitable setpoints, and (3) the search within the feasible region leads to it higher fidelity model of thi s region when the input/output data from consecutive process iterations are used for learning. The KBT Method's utility is demonstrated in production of digital video disks (DVDs).